Abstract
Due to extremely chemical complexity, identification of potential toxicity-related constituents from an herbal medicine (HM) still remains challenging. Traditional toxicity-guided separation procedure suffers from time- and labor-consumption and neglects the additive effect of multi-components. In this study, we proposed a screening strategy called “hepatotoxic equivalent combinatorial markers (HECMs)” for a hepatotoxic HM, Dioscorea bulbifera tuber (DBT). Firstly, the chemical constituents in DBT extract were globally characterized. Secondly, the fingerprints of DBT extracts were established and their in vivo hepatotoxicities were tested. Thirdly, three chemometric tools including partial least squares regression (PLSR), back propagation-artificial neural network (BP-ANN) and cluster analysis were applied to model the fingerprint-hepatotoxicity relationship and to screen hepatotoxicity-related markers. Finally, the chemical combination of markers was subjected to hepatotoxic equivalence evaluation. A total of 40 compounds were detected or tentatively characterized. Two diterpenoid lactones, 8-epidiosbulbin E acetate (EEA) and diosbulbin B (DIOB), were discovered as the most hepatotoxicity-related markers. The chemical combination of EEA and DIOB, reflecting the whole hepatotoxicity of original DBT extract with considerable confidential interval, was verified as HECMs for DBT. The present study is expected not only to efficiently discover hepatotoxicity-related markers of HMs, but also to rationally evaluate/predict the hepatotoxicity of HMs.
Highlights
The traditional approach to identify toxic ingredients from an herbal medicine (HM) often involves two steps: (1) chemical isolation from the plant extract, and (2) toxic evaluation of each isolates[6,7]
Inspired by the aforementioned studies, we attempted to explant the concept of bioactive equivalent combinatorial components (BECCs) to discover the “hepatotoxic equivalent combinatorial markers (HECMs)” for the hepatotoxicity evaluation of Dioscorea bulbifera tuber (DBT)
An UHPLC-fingerprint analysis with diode array detector was developed for chemical consistency evaluation of 21 batches of DBT samples; (2) the in vivo hepatotoxic effects of DBT samples were tested in terms of serum alanine aminotransferase (ALT) and aspartate transaminase (AST) in mice; (3) the fingerprint-toxicity correlation was modeled by multivariate statistical analysis including partial least squares regression (PLSR), back propagation-artificial neural network (BP-ANN) and cluster analysis, the hepatotoxicity-related constituents responsible for the whole toxicity of DBT were screened out; (4) the chemical combination of two diterpenoid lactones was discovered and confirmed as HECMs of DBT, which could be applied to estimate the potential toxicity of DBT samples
Summary
The traditional approach to identify toxic ingredients from an HM often involves two steps: (1) chemical isolation from the plant extract, and (2) toxic evaluation of each isolates[6,7]. Some pioneering works in the field of efficacy evaluation of HM have been launched, where a meaningful term “bioactive equivalent combinatorial components (BECCs)” representative of the holistic effect of an HM26–29 has been proposed for the first time. Inspired by the aforementioned studies, we attempted to explant the concept of BECCs to discover the “hepatotoxic equivalent combinatorial markers (HECMs)” for the hepatotoxicity evaluation of DBT. This strategy mainly includes the following four steps (Fig. 1): (1) an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MS) method was established for characterizing the chemical constituents in DBT extract. An UHPLC-fingerprint analysis with diode array detector was developed for chemical consistency evaluation of 21 batches of DBT samples; (2) the in vivo hepatotoxic effects of DBT samples were tested in terms of serum alanine aminotransferase (ALT) and aspartate transaminase (AST) in mice; (3) the fingerprint-toxicity correlation was modeled by multivariate statistical analysis including partial least squares regression (PLSR), back propagation-artificial neural network (BP-ANN) and cluster analysis, the hepatotoxicity-related constituents responsible for the whole toxicity of DBT were screened out; (4) the chemical combination of two diterpenoid lactones was discovered and confirmed as HECMs of DBT, which could be applied to estimate the potential toxicity of DBT samples
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